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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/21118983
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. 2010 Dec 14;107(50):21767-72.
doi: 10.1073/pnas.0908104107. Epub 2010 Nov 30.

How the brain integrates costs and benefits during decision making

Ulrike Basten  1 , Guido Biele, Hauke R Heekeren, Christian J Fiebach
Affiliations

How the brain integrates costs and benefits during decision making

Ulrike Basten et al. Proc Natl Acad Sci U S A. .

Abstract

When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost-benefit comparison can be explained as a stochastic accumulation of cost-benefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Diffusion model, behavioral paradigm, and training. (A) Schematic display of the diffusion model. The drift process has a neutral starting point between the two decision boundaries. Traces exemplify decision processes with high (blue) and low (green) drift rates, in which the difference of benefits minus costs drifts toward either an accept response (upper boundary) or a rejection (lower boundary). In this example, rejection decisions are interpreted as errors, because benefits outweigh costs. Response-time distributions show that lower drift rates are associated with slower accumulation processes (as the blue distribution is shifted nearer to the starting point than the green one) and higher error rates (as the green distribution has a larger mass in the rejections/errors). (B) Task paradigm with color/shape stimuli that are associated with different ranges of monetary benefits and losses. For example, a yellow square might be associated with a reward between 2 and 2.4 € and a loss between 0.4 and 0.8 € (see Fig. S1 for more details). Successful cost–benefit integration would lead participants to accept this stimulus. Null trials are variable intertrial intervals. (C) Training (SI Methods) involved three blocks each for colors (Left) and shapes (Right), each of which comprised 56 stimulus pairs. Participants first compared the presented pairs and then received feedback as shown, thus implicitly learning the value ranges associated with colors and shapes. The last two blocks were terminated when a criterion (95% correct) was reached, but never before 25 trials were completed. (D) Accuracy for the training session, displayed according to training block (x axis) and reward (green) versus loss (red) condition. Error bars represent SEM.
Fig. 2.
Fig. 2.
Behavioral results and model fit. (A) Categorical cost–benefit differences (ranging from −5 to +5) associated with stimuli from each cost–benefit difference category. Axes denote the eight categories of monetary benefits (x axis) and costs (y axis) from which the stimuli were generated. A more detailed version is found in Fig. S1. (B and C) Response times (RT; in seconds) and accuracy in percent correct, respectively, per category. (D and E) Fitted chronometric (D) and psychometric functions (E) together with observed data, for four exemplary participants. The x axis in these plots represents the five cost–benefit difference categories (A; Fig. S1) independent of the sign of the cost–benefit difference. The boxes (RT) and circles (accuracy) represent the observed data points, and lines represent the fitted functions. Model fits for all subjects are shown in Fig. S2.
Fig. 3.
Fig. 3.
Brain activation results for cost–benefit comparison. (A) Localization of the ventral striatal (vStr) ROI, whose average time course was used to derive the neural difference signal Dneural = tvStr – tAmyg. (B) Localization of the amygdala ROI, whose average time course was used to derive the neural difference signal Dneural = tvStr – tAmyg. Activation results in A and B are shown at P < 0.005 (uncorrected). (C) Brain regions whose BOLD signals are coupled to the neural difference signal Dneural (identified using PPI analysis) and additionally modulated by the individual median drift rate (Inset), displayed at whole-brain corrected P < 0.05 (Methods). SFS, superior frontal sulcus.
Fig. 4.
Fig. 4.
Brain activation results for accumulation of the cost–benefit difference. (A) The diffusion model predicts greater BOLD signal in accumulator regions for harder decisions. (Left) Drift processes with a high (blue), medium (green), or low (yellow) drift rate μ. Lower drift rates signify harder decisions (within participants) or less efficient decision making (between participants). Because low-drift-rate decisions last longer, the area under the drift process (∫Σ) will also be greater for lower drift rates. Therefore, convolving the drift process with a hemodynamic response function leads to greater predicted BOLD signals for harder trials (Right). Note that accumulator regions should reflect the absolute value of the area under the drift process, as they are indifferent to the sign of the cost–benefit difference. (B) Accumulator regions are functionally coupled to the comparator region (VMPFC), modulated by individual drift rates, and are more strongly activated for more difficult trials as described in A. Accumulator regions in mid-intraparietal sulcus were identified by a conjunction analysis (see Methods for details). (C) Proposed brain mechanism for cost-benefit decision making: Cost and benefit signals from amygdala and nucleus accumbens (NAcc), respectively, are compared in VMPFC. The resulting difference signal is accumulated in intraparietal sulcus (IPS) until a decision threshold is reached.
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